Machine Learning Interview Questions (Level -Hard)

[vishrut-singhal]

1) What are the two classification methods that SVM ( Support Vector Machine) can
handle?
a) Combining binary classifiers
b) Modifying binary to incorporate multiclass learning
12) What is ensemble learning?
To solve a particular computational program, multiple models such as classifiers or experts are
strategically generated and combined. This process is known as ensemble learning.
13) Why ensemble learning is used?
Ensemble learning is used to improve the classification, prediction, function approximation etc of a
model.
14) When to use ensemble learning?
Ensemble learning is used when you build component classifiers that are more accurate and
independent from each other.
15) What are the two paradigms of ensemble methods?
The two paradigms of ensemble methods are
a) Sequential ensemble methods
b) Parallel ensemble methods
16) What is the general principle of an ensemble method and what is bagging and
boosting in ensemble method?
The general principle of an ensemble method is to combine the predictions of several models built
with a given learning algorithm in order to improve robustness over a single model. Bagging is a
method in ensemble for improving unstable estimation or classification schemes. While boosting
method are used sequentially to reduce the bias of the combined model. Boosting and Bagging both
can reduce errors by reducing the variance term.
17) What is bias-variance decomposition of classification error in ensemble method?
The expected error of a learning algorithm can be decomposed into bias and variance. A bias term
measures how closely the average classifier produced by the learning algorithm matches the target
function. The variance term measures how much the learning algorithm’s prediction fluctuates for
different training sets.
18) What is an Incremental Learning algorithm in ensemble?
Incremental learning method is the ability of an algorithm to learn from new data that may be
available after classifier has already been generated from already available dataset.
19) What is PCA, KPCA and ICA used for?
PCA (Principal Components Analysis), KPCA ( Kernel based Principal Component Analysis) and ICA (
Independent Component Analysis) are important feature extraction techniques used for
dimensionality reduction.
20) What is dimension reduction in Machine Learning?
In Machine Learning and statistics, dimension reduction is the process of reducing the number of
random variables under considerations and can be divided into feature selection and feature
extraction
21) What are support vector machines?
Support vector machines are supervised learning algorithms used for classification and regression
analysis.
22) What are the components of relational evaluation techniques?
The important components of relational evaluation techniques are
a) Data Acquisition
b) Ground Truth Acquisition
c) Cross Validation Technique
d) Query Type
e) Scoring Metric
f) Significance Test
23) What are the different methods for Sequential Supervised Learning?
The different methods to solve Sequential Supervised Learning problems are
a) Sliding-window methods
b) Recurrent sliding windows
c) Hidden Markow models
d) Maximum entropy Markow models
e) Conditional random fieldsf) Graph transformer networks
24) What are the areas in robotics and information processing where sequential
prediction problem arises?
The areas in robotics and information processing where sequential prediction problem arises are
a) Imitation Learning
b) Structured prediction
c) Model based reinforcement learning
25) What is batch statistical learning?
Statistical learning techniques allow learning a function or predictor from a set of observed data that
can make predictions about unseen or future data. These techniques provide guarantees on the
performance of the learned predictor on the future unseen data based on a statistical assumption on
the data generating process.
26) What is PAC Learning?
PAC (Probably Approximately Correct) learning is a learning framework that has been introduced to
analyze learning algorithms and their statistical efficiency.
27) What are the different categories you can categorized the sequence learning
process?
a) Sequence prediction
b) Sequence generation
c) Sequence recognition
d) Sequential decision
28) What is sequence learning?
Sequence learning is a method of teaching and learning in a logical manner.
29) What are two techniques of Machine Learning ?
The two techniques of Machine Learning are
a) Genetic Programming
b) Inductive Learning